PASTA: Pathology-Aware MRI to PET Cross-Modal Translation with Diffusion Models

TL;DR

PASTA is a novel diffusion model-based framework that generates high-quality, pathology-preserving synthetic PET images from MRI scans, improving diagnostic utility and Alzheimer's classification accuracy.

Contribution

Introduces PASTA, a pathology-aware cross-modal translation method using diffusion models with a dual-arm architecture and multi-modal integration for superior PET synthesis.

Findings

Synthesized PET achieves top quantitative scores.

Preserves structural and pathological details effectively.

Improves Alzheimer's classification accuracy by 4% over MRI.

Abstract

Positron emission tomography (PET) is a well-established functional imaging technique for diagnosing brain disorders. However, PET's high costs and radiation exposure limit its widespread use. In contrast, magnetic resonance imaging (MRI) does not have these limitations. Although it also captures neurodegenerative changes, MRI is a less sensitive diagnostic tool than PET. To close this gap, we aim to generate synthetic PET from MRI. Herewith, we introduce PASTA, a novel pathology-aware image translation framework based on conditional diffusion models. Compared to the state-of-the-art methods, PASTA excels in preserving both structural and pathological details in the target modality, which is achieved through its highly interactive dual-arm architecture and multi-modal condition integration. A cycle exchange consistency and volumetric generation strategy elevate PASTA's capability to produce high-quality 3D PET scans. Our qualitative and quantitative results confirm that the synthesized PET scans from PASTA not only reach the best quantitative scores but also preserve the pathology correctly. For Alzheimer's classification, the performance of synthesized scans improves over MRI by 4%, almost reaching the performance of actual PET. Code is available at https://github.com/ai-med/PASTA.